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Improving motor imagery classification during induced motor perturbations.
Vidaurre, C; Jorajuría, T; Ramos-Murguialday, A; Müller, K-R; Gómez, M; Nikulin, V V.
Afiliação
  • Vidaurre C; Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.
  • Jorajuría T; Machine Learning Group, Computer Science Faculty, Berlin Institute of Technology, Berlin, Germany.
  • Ramos-Murguialday A; Both authors contributed equally.
  • Müller KR; Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.
  • Gómez M; Both authors contributed equally.
  • Nikulin VV; Institute for Medical Psychology and Behavioral Neurobiology (IMP), University of Tübingen, 72076 Tübingen, Germany.
J Neural Eng ; 18(4)2021 07 21.
Article em En | MEDLINE | ID: mdl-34233305
ABSTRACT
Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Eletroencefalografia / Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Eletroencefalografia / Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article